基于多光谱技术的大白菜叶色快速分类及量化研究
Rapid color classification and quantification of Chinese cabbage leaf based on multispectral technique
何杨帆 1汪焕悦 1张君 1刘传峰 2孙磊 1索雪松 1范晓飞1
作者信息
- 1. 河北农业大学 机电工程学院,河北 保定 071001
- 2. 河北农业大学 信息科学与技术学院,河北 保定 071001
- 折叠
摘要
为解决目前人工鉴定大白菜叶色主观性强、速度慢、效率低等问题,本研究提出了一种多光谱图像处理结合机器视觉技术对大白菜叶色快速、准确分类和量化的方法.结果表明:由 19 通道多光谱系统提取到的原始光谱数据包含信息更全面更准确,由其所建立的SVM分类模型具有最优的分类效果,训练集准确率是98.24%,验证集准确率是 87.18%.运用连续投影算法(SPA)提取特征波长进行分析,选择用 5 通道相机采集的白菜样本继续研究大白菜叶色的量化.通过提取其RGB、HSV、LAB 9 个颜色特征值进行数据处理后可以准确地将大白菜叶色进行 0～100 间数值量化.
Abstract
In order to solve the problems of strong subjectivity,slow speed and low efficiency in manual identification of leaf color of Chinese cabbage,this study proposed a method of rapid and accurate classification and quantification of leaf color of Chinese cabbage by combining multi-spectral image processing with machine vision technology.The results showed that the original spectral data extracted by the 19-channel multispectral system contained more comprehensive and accurate information,and the SVM classification model established by the system showed the best classification effect.The accuracy of training set was 98.24%,and the accuracy of verification set was 87.18%.The continuous projection algorithm(SPA)was used to extract characteristic wavelength for analysis,and the Chinese cabbage samples collected by a 5-channel camera were selected to continue to continuously study the quantification of leaf color of Chinese cabbage.By extracting the RGB,HSV,LAB nine color feature values for data processing,the color of Chinese cabbage leaves can be accurately quantized by 0-100 values.
关键词
多光谱成像/机器视觉/大白菜叶色/分类/量化Key words
Multispectral imaging/machine vision/cabbage leaf color/classification/quantification引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据